shape for dimensions in Pandas - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of shape attribute on DataFrame

What is the output of the following code snippet?

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2, 3],
    'B': [4, 5, 6],
    'C': [7, 8, 9]
})

print(df.shape)

A (9, 1)

B (3,)

C (1, 9)

D (3, 3)

💡 Hint

The shape attribute returns a tuple with (rows, columns).

The DataFrame has 3 rows and 3 columns, so df.shape returns (3, 3).

❓ data_output

intermediate

Shape of a Series object

What is the output of the shape attribute for the following pandas Series?

Pandas

import pandas as pd

s = pd.Series([10, 20, 30, 40])
print(s.shape)

A (1, 4)

B 4

C (4,)

D (4, 1)

💡 Hint

Series shape is a tuple with one element representing length.

A pandas Series is one-dimensional, so its shape is a tuple with one element: (4,).

🔧 Debug

advanced

Error when accessing shape of a list

What error will this code produce?

Pandas

my_list = [1, 2, 3, 4]
print(my_list.shape)

A AttributeError: 'list' object has no attribute 'shape'

B TypeError: 'list' object is not callable

C NameError: name 'shape' is not defined

D IndexError: list index out of range

💡 Hint

Lists in Python do not have a shape attribute.

Python lists do not have a shape attribute, so trying to access it raises an AttributeError.

🧠 Conceptual

advanced

Understanding shape for 3D numpy arrays

If a numpy array has shape (4, 3, 2), what does each number represent?

A 4 layers, 3 rows, 2 columns

B 4 rows, 3 columns, 2 layers

C 4 columns, 3 rows, 2 layers

D 4 rows, 2 columns, 3 layers

💡 Hint

Shape is given as (layers, rows, columns) for 3D arrays.

For a 3D numpy array, the shape is (layers, rows, columns). So (4, 3, 2) means 4 layers, each with 3 rows and 2 columns.

🚀 Application

expert

Determining shape after DataFrame filtering

Given the DataFrame below, what is the shape after filtering rows where 'Age' > 30?

Pandas

import pandas as pd

data = {'Name': ['Anna', 'Bob', 'Cara', 'Dave'],
        'Age': [25, 35, 45, 28],
        'City': ['NY', 'LA', 'NY', 'SF']}
df = pd.DataFrame(data)

filtered_df = df[df['Age'] > 30]
print(filtered_df.shape)

A (3, 3)

B (2, 3)

C (2, 2)

D (3, 2)

💡 Hint

Count how many rows have Age greater than 30 and remember columns stay the same.

Only 'Bob' and 'Cara' have Age > 30, so 2 rows remain. The DataFrame has 3 columns, so shape is (2, 3).